Image style transfer is a remarkable research hotspot in computer image processing.
However, state-of-the-art models have some drawbacks, such as low efficiency of transfer …

Existing CNN-based style transfer frameworks have suffered from inaccurate control of pixel-
wise stylization. As the CNN operation is designed based on kernel-wise operation, such a …

Neural style transfer has drawn broad attention in recent years. However, most existing
methods aim to explicitly model the transformation between different styles, and the learned …

This work presents Unified Contrastive Arbitrary Style Transfer (UCAST), a novel style
representation learning and transfer framework, that can fit in most existing arbitrary image …

Despite the recent rapid development of neural style transfer, existing style transfer methods
are still somewhat inefficient or have a large model size, which limits their application on …

Neural learning methods have been shown to be effective in style transfer. These methods,
which are called NST, aim to synthesize a new image that retains the high-level structure of …

This paper introduces a deep learning approach to photorealistic universal style transfer that
extends the PhotoNet network architecture by adding extra feature-aggregation modules …

Real‐time arbitrary style transfer is based on a feed‐forward network, which consists of a pre‐
trained encoder, a feature transformer, and a trainable decoder. However, the previous …

Arbitrary image style transfer is the process of inputting any set of images to generate
images with a certain artistic style. Aiming at the problem of how to adapt both global style …

Recent neural style transfer frameworks have obtained astonishing visual quality and
flexibility in Single‐style Transfer (SST), but little attention has been paid to Multi‐style …